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Direct multiperiod forecasting for algorithmic trading
Author(s) -
Kawakatsu Hiroyuki
Publication year - 2018
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2488
Subject(s) - iterated function , econometrics , context (archaeology) , sequence (biology) , computer science , set (abstract data type) , selection (genetic algorithm) , economics , mathematics , artificial intelligence , mathematical analysis , paleontology , genetics , biology , programming language
This paper examines the performance of iterated and direct forecasts for the number of shares traded in high‐frequency intraday data. Constructing direct forecasts in the context of formulating volume weighted average price trading strategies requires the generation of a sequence of multistep‐ahead forecasts. I discuss nonlinear transformations to ensure nonnegative forecasts and lag length selection for generating a sequence of direct forecasts. In contrast to the literature based on low‐frequency macroeconomic data, I find that direct multiperiod forecasts can outperform iterated forecasts when the conditioning information set is dynamically updated in real time.